Density function centric signal processing

ABSTRACT

A circuit for direct current (DC) offset estimation comprises a quantile value circuit and a signal processor. The quantile value circuit determines a plurality of quantile values of an input signal and includes a plurality of quantile filters. Each quantile filter includes a comparator, a level shifter, a monotonic transfer function component, and a latched integrator. The comparator compares the input signal and a quantile value. The level shifter shifts the output of the comparator. The monotonic transfer function component determines the magnitude of the shifted signal and provide a transfer function signal. The latched integrator suppresses transient characteristics of the transfer function signal and provide the quantile value. The signal processor is configured to calculate a weighted average of the quantile values to yield a DC offset estimate.

RELATED APPLICATIONS

The current non-provisional patent application claims priority benefit,with regard to all common subject matter, of an earlier-filed U.S.provisional patent application titled “DENSITY FUNCTION CENTRIC SIGNALPROCESSING”, application Ser. No. 61/729,112, filed Nov. 21, 2012. Theearlier-filed application is hereby incorporated by reference into thecurrent application in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Embodiments of the current invention relate to methods and systems ofcommunication that utilize statistical probability functions to performsignal processing.

2. Description of the Related Art

Communications systems allow data, audio, and video to be transmittedfrom one point to another through links implemented in terrestrial,cellular, satellite, and storage systems. Each link may include a signaltransmitter, a noise component, a channel, and a signal receiver. Thesignal transmitter may encode and/or modulate data bits into a signalbefore transmitting the signal along the channel. The receiver mayreceive the signal and attempt to demodulate and decode it in order torecover the original data. Noise can affect the signal at thetransmitter, on the channel, at the receiver, or all three. Theperformance of the receiver is particularly sensitive to noise as it maynot recover the original data accurately if the noise cannot be properlydetected and mitigated. Prior art receiver designs include schemes todetect and mitigate noise. However, the receivers have difficultiesdealing with noise when the noise varies with time, is impulsive, or isnon-Gaussian in nature.

SUMMARY OF THE INVENTION

Embodiments of the current invention solve the above-mentioned problemsand provide a distinct advance in the art of communications systems.More particularly, embodiments of the invention provide methods andcircuits that improve the performance of communications systems that aresubject to time-varying noise.

A first embodiment of the invention provides a circuit for directcurrent (DC) offset estimation. The circuit comprises a quantile valuecircuit and a signal processor. The quantile value circuit determines aplurality of quantile values of an input signal and includes a pluralityof quantile filters. Each quantile filter includes a comparator, a levelshifter, a monotonic transfer function component, and a latchedintegrator. The comparator compares the input signal and a quantilevalue. The level shifter shifts the output of the comparator. Themonotonic transfer function component determines the magnitude of theshifted signal and provide a transfer function signal. The latchedintegrator suppresses transient characteristics of the transfer functionsignal and provide the quantile value. The signal processor isconfigured to calculate a weighted average of the quantile values toyield a DC offset estimate.

A second embodiment of the invention provides a circuit for fine DCoffset estimation. The circuit comprises a DC offset estimation circuit,an adaptive prescaler, and a selector circuit. The DC offset estimationcircuit provides a DC offset estimate of an input signal and may includethe circuit of the first embodiment of the current invention. Theadaptive prescaler scales the DC offset estimate signal in order toprovide a scaled signal. The selector circuit receives the input signaland the scaled signal and provides the scaled signal to the DC offsetestimation circuit in order for the DC offset estimation circuit toperform a fine DC offset estimation of the scaled signal.

A third embodiment of the invention provides a decoder apparatus forreceiving a signal from a channel medium. The decoder apparatuscomprises a noise detector and a decoder. The noise detector receives asignal from a channel density function estimation circuit, wherein thenoise detector estimates a signal density function, maps the densityfunction in one or more density function bit samples, and appends adensity function bit reliability measure to each bit sample. The decoderdetermines logic states of the appended density function bit reliabilitymeasure and detected bits in the signal and applies the density functionbit reliability measures of detected bits to generate data bits.

A fourth embodiment of the invention provides an apparatus forclassifying a signal as one of noise and a predetermined type of eventcomprising a plurality of symbols subjected to predetermined channelnoise regimes, each regime represented by one or more of a plurality oftemporal attributes. The apparatus comprises a receiver and a signalprocessor. The receiver estimates a density function of the signal andsamples the density function at a plurality of points. The signalprocessor is connected to the receiver and configured to determine amaximum number of the regimes and a minimum number of the regimesthrough a clustering technique, initialize a plurality of clusterscorresponding to the plurality of sampled points, based upon the maximumand minimum numbers, process, through a clustering technique, a signalvalue corresponding to each of the regimes by updating parametersmaintained for a cluster of the plurality of clusters, calculate aplurality of statistical values from the updated parameters, andclassify the signal based upon the plurality of statistical values andthe updated parameters.

A fifth embodiment of the invention provides a method of DC voltageoffset estimation for a DC estimation circuit receiving an input signal.The method comprises the steps of: determining a plurality of quantilevalues of the input signal, calculating a weighted average of thequantile values, compensating the input signal using the weightedaverage, and scaling the compensated input signal.

A sixth embodiment of the invention provides a method of processing anoise signal with functions including automatic squelch, automatic gaincontrol (AGC), equalization, and dynamic range control (DRC). The methodcomprises the steps of: extracting a density function of the noisesignal, extracting an attribute of the density function, producing aunidirectional density function signal level as a function of theattribute, storing the unidirectional density function signal level inresponse to a predetermined condition producing a density functionstored signal level, extracting the density function of the noise signalafter the unidirectional density function signal level is stored,extracting an attribute of the density function, producing aunidirectional density function signal level as a function of theattribute, comparing the most recently produced unidirectional signallevel to the stored signal level, and generating a control signal whenthe unidirectional signal level is a function of the stored signallevel.

A seventh embodiment of the invention provides a method of decoding asignal received from a channel. The method comprises the steps of:determining a density function of a received signal, determining a biasattribute based on the density function of the signal, determining areliability bias for the bias attribute, applying the reliability biasto a reliability measure to estimate logic states of detected bits inthe signal, and generating user bits as a function of the detected bits.

An eighth embodiment of the invention provides a method of classifying asignal as one of noise and a predetermined type of event comprising aplurality of symbols subjected to predetermined channel noise regimes,each regime represented by one or more of a plurality of temporalattributes. The method comprises the steps of: estimating a densityfunction of the signal, sampling the density function at a plurality ofpoints, determining a maximum number of the regimes and a minimum numberof the regimes through a clustering technique, initializing a pluralityof clusters corresponding to the sampled points, based upon the maximumand minimum numbers, processing, through a clustering technique, asignal value corresponding to each of the regimes by updating parametersmaintained for a cluster, calculating a plurality of statistical valuesfrom the parameters maintained for the clusters, and classifying thesignal based upon the plurality of statistical values and theparameters.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. Other aspectsand advantages of the current invention will be apparent from thefollowing detailed description of the embodiments and the accompanyingdrawing figures.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

Embodiments of the current invention are described in detail below withreference to the attached drawing figures, wherein:

FIG. 1 is a schematic block diagram of a circuit for direct current (DC)offset estimation, constructed in accordance with a first embodiment ofthe current invention;

FIG. 2 is a schematic block diagram of a quantile value circuit utilizedin the circuit of FIG. 1;

FIG. 3 is a plot of a probability density function (PDF) of an attributeof a communications signal;

FIG. 4 is a schematic block diagram of a circuit for fine DC offsetestimation, constructed in accordance with a second embodiment of thecurrent invention;

FIG. 5 is a schematic block diagram of a decoder apparatus, constructedin accordance with a third embodiment of the current invention;

FIG. 6 is a schematic block diagram of an apparatus for classifying acommunications signal, constructed in accordance with a fourthembodiment of the current invention;

FIG. 7 is a schematic block diagram of a system in which embodiments ofthe current invention may be utilized;

FIG. 8 is a schematic block diagram of a system in which embodiments ofthe current invention may be utilized;

FIG. 9 is a schematic block diagram of a system in which embodiments ofthe current invention may be utilized;

FIG. 10 is a flow diagram of at least a portion of the steps of a methodof DC offset estimation in accordance with a fifth embodiment of thecurrent invention;

FIG. 11 is a flow diagram of at least a portion of the steps of a methodof processing a noise signal with functions including automatic squelch,automatic gain control (AGC), and dynamic range control (DRC) inaccordance with a sixth embodiment of the current invention;

FIG. 12 is a flow diagram of at least a portion of the steps of a methodof decoding a signal received from a channel in accordance with aseventh embodiment of the current invention; and

FIG. 13 is a flow diagram of at least a portion of the steps of a methodof classifying a signal as one of noise and a predetermined type ofevent comprising a plurality of symbols subjected to predeterminedchannel noise regimes, each regime represented by one or more of aplurality of temporal attributes in accordance with a eighth embodimentof the current invention.

The drawing figures do not limit the current invention to the specificembodiments disclosed and described herein. The drawings are notnecessarily to scale, emphasis instead being placed upon clearlyillustrating the principles of the invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The following detailed description of the invention references theaccompanying drawings that illustrate specific embodiments in which theinvention can be practiced. The embodiments are intended to describeaspects of the invention in sufficient detail to enable those skilled inthe art to practice the invention. Other embodiments can be utilized andchanges can be made without departing from the scope of the presentinvention. The following detailed description is, therefore, not to betaken in a limiting sense. The scope of the present invention is definedonly by the appended claims, along with the full scope of equivalents towhich such claims are entitled.

In this description, references to “one embodiment”, “an embodiment”, or“embodiments” mean that the feature or features being referred to areincluded in at least one embodiment of the technology. Separatereferences to “one embodiment”, “an embodiment”, or “embodiments” inthis description do not necessarily refer to the same embodiment and arealso not mutually exclusive unless so stated and/or except as will bereadily apparent to those skilled in the art from the description. Forexample, a feature, structure, act, etc. described in one embodiment mayalso be included in other embodiments, but is not necessarily included.Thus, the current technology can include a variety of combinationsand/or integrations of the embodiments described herein.

A circuit 10 for direct current (DC) offset estimation, constructed inaccordance with a first embodiment of the current invention, is shown inFIG. 1, and may broadly comprise a quantile value circuit 12 and asignal processor 14. The circuit 10 may be utilized in a device that ispart of a wired or wireless communications system, such as telephone,internet, and television services in a residence or business, satellitecommunications, cellular communications, wireless networkcommunications, and the like. The device may be a component such as acable or digital subscriber line (DSL) modem, a router, a handset, orthe like that is subjected to noise or interference from any of avariety of sources. The device may also be a component of a sensorsystem, such as a voltage meter, a current meter, or a power meter. Thecircuit 10 may be specifically utilized with a receiver of the devicethat receives a communications signal. The signal typically is periodicand sinusoidal or square-wave in nature with high values and low valuesthat represent data bits. The signal also has a DC level whichrepresents a baseline or reference voltage such that the high and lowvalues are evaluated with respect to the DC level. Normally, the DClevel is constant. However, noise impinging on the signal may cause theDC level to vary—introducing a DC offset that affects and changes thehigh value, the low value, or both. Receivers may be sensitive to noisethat creates the DC offset because even small values of DC offset canlead to errors in decoding the signal data. The DC offset may becorrected by other circuitry. In order for the other circuitry tocorrect the DC offset, an estimate of the offset must be supplied.

The quantile value circuit 12, as shown in FIG. 2, generally determinesa plurality of quantile values 16 of an input communications signal 18.The quantile value 16 is a value of a certain attribute of the signalsuch as the voltage, the current, the power, or the like. Theprobability that the value of the attribute of the signal is less thanthe quantile value 16 is inversely proportional to the number n ofquantile values 16, as seen in FIG. 3, wherein a probability densityfunction (PDF) of an attribute of the communications signal 18 is shown.Quantiles may also refer to points taken at regular interval from thecumulative distribution function (CDF) of a variable, such as thecommunications signal 18. Furthermore, a quantile set of points maydivide an ordered distribution into parts, regardless of the proportionof outcomes in each part or their position on a grid. The quantile valuecircuit 12 may also perform any one of the following functions:multimodal pulse shaping, analog rank filtering, offset rank ordering,analog counting, clustering, singular value decomposition, principalcomponent analysis, or independent component analysis. As seen in FIG.2, the quantile value circuit 12 may include a plurality of quantilefilters 20, wherein each quantile filter 20 includes a comparator 22, alevel shifter 24, a monotonic transfer function component 26, and alatched integrator 28. Each quantile filter 20 may produce a quantilevalue

The comparator 22 generally compares two signals and produces an outputthat is the difference between the two. The comparator 22 may include apositive input to which the communications signal 18 is connected and anegative input to which the quantile value 16 is connected. Thecomparator 22 may output a difference signal 30 corresponding to thecommunications signal 18 minus the quantile value 16. The comparator 22may also receive an optional first control signal Z, which is generallya known reference signal from within the circuit 10 or generatedexternal to the circuit 10. The first control signal Z may have a DCvoltage or an alternating current (AC) voltage or may be periodic innature with a constant or varying frequency or an arbitrary but knownwaveform. In addition, the first control signal Z may introduce controlor timing parameters such as filtering the communications signal 18 overa certain time period or controlling the response time over which thecomparator 22 functions. The comparator 22 may include analog activeand/or passive electronic circuitry, such as operational amplifiers,filters, and the like, or combinations thereof.

The level shifter 24 generally shifts the level of a certain aspect ofthe difference signal 30 such as the voltage or the current. The levelshifter 24 may include analog active and/or passive electroniccircuitry, such as operational amplifiers, transistors, discretecomponents, and the like, or combinations thereof. In some embodiments,the level shifter 24 may further include signal mixer components whichmay change or shift the frequency of the difference signal 30. The levelshifter 24 may also receive an optional second control signal Q, withsimilar features to the first control signal Z.

The monotonic transfer function component 26 generally produces amagnitude or norm of a signal by applying a monotonic transfer functionsuch as an exponential function or a logarithmic function that may havea filtering effect. The monotonic transfer function component 26 mayinclude analog active and/or passive electronic circuitry. The monotonictransfer function component 26 may receive a signal from the levelshifter 24.

The latched integrator 28 generally reduces the amplitude of transientcharacteristics of the signal from the monotonic transfer functioncomponent 26. The latched integrator 28 may control the rate at whichthe signal from the monotonic transfer function component 26 is tracked.The latched integrator 28 may also receive, or be configured with,timing information regarding the structure of the packets or payloads ofthe communications signal 18. Each packet may include a header portionand a data portion. The function of the latched integrator 28 may changedepending on the portion of the packet being processed. For example, thelatched integrator 28 may provide greater suppression of the transientcharacteristics during the data portion of the packet than during theheader portion of the packet. The latched integrator 28 may includeanalog active and/or passive electronic circuitry that performs anintegrating function. The latched integrator 28 produces the quantilevalue 16, which is fed back to the comparator 22.

The signal processor 14 generally calculates a weighted average of thequantile values to yield a DC offset estimate 32 and may includeprocessors, microprocessors, microcontrollers, digital signal processors(DSPs), field-programmable gate arrays (FPGAs), analog and/or digitalapplication-specific integrated circuits (ASICs), or the like, orcombinations thereof. The signal processor 14 may generally execute,process, or run instructions, code, code segments, software, firmware,programs, applications, apps, processes, services, daemons, or the like,or may step through states of a finite-state machine. The signalprocessor 14 may receive all of the quantile values 16 from the quantilevalue circuit 10 and may determine the average of the quantile values 16by weighting some of the values according to the probability densityfunction, the cumulative distribution function, other density function,or other mathematical function in order to output an estimate of the DCoffset of a certain attribute of the communications signal 18.

A circuit 100 for fine DC offset estimation, constructed in accordancewith a second embodiment of the current invention, is shown in FIG. 4,and may broadly comprise a selector circuit 102, a DC offset estimator104, and an adaptive prescaler 106. The circuit 100 may receive thecommunications signal 18 and may output a fine DC offset estimate 108.The circuit 100 may be utilized in the same fashion as the circuit 10and may perform a similar function, except that the fine DC offsetestimate 108 may provide a higher resolution than the DC offset estimate32.

The selector circuit 102 generally selects one of two or more signals topass through to its output. The selector circuit 102 may receive thecommunications signal 18 at a first input and the fine DC offsetestimate 108 at a second input. The selector circuit 102 may outputeither the communications signal 18 or the fine DC offset estimate 108depending on the state of a select line 110, which may have a binaryvalue to select either the first or the second input. The selectorcircuit 102 may include multiplexing components, switching components,operational amplifiers, discrete components, or the like, orcombinations thereof.

The DC offset estimator 104 generally provides an estimate of the DCoffset of a certain attribute of the output of the selector circuit 102,which is either the communications signal 18 or the fine DC offsetestimate 108. The DC offset estimator 104 has substantially the samestructure and functions in substantially the same manner as the circuit10, discussed above.

The adaptive prescaler 106 generally scales the frequency of the signalfrom the DC offset estimator 104. The adaptive prescaler 106 may includecounters, filters, operational amplifiers, discrete components, or thelike, or combinations thereof. The adaptive prescaler 106 may receivethe signal from the DC offset estimator 104 and may reduce the frequencyof the signal by an integer factor in order to output the fine DC offsetestimate 108.

A decoder apparatus 200, constructed in accordance with a thirdembodiment of the current invention, is shown in FIG. 5, and may broadlycomprise a channel density function estimation circuit 202, a noisedetector 204, and a decoder 206. The decoder apparatus 200 may beutilized in a communication system when receiving a signal on a channelthat has already been encoded by an encoder 208 with a symbol-basedencoding or modulation scheme, such as frequency or phase shift keying,quadrature amplitude modulation, or the like. The system may alsoutilize forward error correction schemes, such as turbo codes orlow-density parity-check codes, which take probabilities as inputsrather than bits. Thus, instead of zeros and ones, the input is thelikelihood each bit is a zero or a one. A decoder typically usesiterative hypothesis testing to arrive at the original message. Bitlikelihoods are derived from symbol likelihoods, which are typicallydetermined by the Euclidian distance a symbol is from the closestnominal symbol value. Symbol values are, in turn, derived from some formof digital modulation of an analog signal. An analog front end of asignal receiving circuit, aware of the noise conditions of a channelduring the reception of a symbol, could influence the likelihood of thesymbol and, hence, the likelihood of its encoded bits. This additionalinformation may be used by the decoder to arrive at the original messagefaster or more reliably.

The encoded data may include intrinsic and extrinsic information.Intrinsic information, such as the Log Likelihood Ratio (LLR), is thesoft information inherent in a bit U received over a channel. It istypically the sample a priori value prior to unconstrained decoding.Intrinsic information is given by the following: λ₁(U)=log₂ {prob(U=1)}−log₂ {Prob (U=0)}.

Extrinsic information is the information provide about a received bitsfrom the other received bits given the code constraints, as in thefollowing: λe(U)=log2 {(prob (U=1|decoding state)}+log2 {Prob(U=0)}−log2 {Prob (U=0| decoding state)}−log2 {prob (U=1)}.

The channel density function estimation circuit 202 may provide anestimation of an aspect of the communications signal 18. The channeldensity function estimation circuit 202 may have a similar structure tothe circuit 10 or the circuit 100 and may function in a similar way.

The noise detector 204 generally adds information to the signal from thechannel density function estimation circuit 202 to assist in decodingthe signal. The noise detector 204 may include analog electroniccircuits, digital electronic circuits, or combinations of both. Thenoise detector 204 may determine or calculate an estimate of a densityfunction, such as the PDF or the CDF, of the signal from the channeldensity function estimation circuit 202. The noise detector 204 may thenmap the density function to one or more density function bit samples andassign a density function bit reliability measure to each of the densityfunction bit samples.

In some embodiments, the noise detector 204 may map the density functionto a specific channel or noise source condition based on one or moreattributes. The noise detector 204 may also introduce a bias componentinto the intrinsic or extrinsic information. This bias, or reality bias,can be imposed on the U=1 bit or the U=0 bit when, for example, tailinformation or median indicates a temporary bias, as in the following:λ₁(U)=log₂ {prob (U=1)+bias}−log₂ {Prob (U=0)}. Or, alternatively:λ₁(U)=log₂ {prob (U=1)+bias}−log₂ {Prob (U=0)+bias}.

In some embodiments, the noise detector 204 may determine or calculatean estimate of a density function, such as the PDF or the CDF, of thesignal from the channel density function estimation circuit 202. Thenoise detector 204 may then map the density function to a specificchannel or noise source condition based on one or more attributes. Thenoise detector 204 may also generate a bit stream V. The conditionalprobability of the soft bit P(U=1|V) and P(U=0|V) estimated. Theextrinsic information is then computed using the U bits as well as the Vbits, as in the following: λ_(e)(U)=log₂ {(prob (U=1| decoding state,V)}+log₂ {Prob (U=0)}−log₂ {Prob (U=0| decoding state, V)}−log₂ {prob(U=1)}.

The decoder 206 generally decodes the signal from the noise detector 204and may include analog electronic circuits, digital electronic circuits,discrete logic components, or combinations thereof. The decoder 206 maydetermine the logic states of the appended density function bit anddetected bits of the communications signal 18. The decoder 206 may alsoapply the appended density function bit reliability measures of thedetected bits in order to generate the data bits.

An apparatus 300 for classifying a communications signal 18, constructedin accordance with a fourth embodiment of the current invention, isshown in FIG. 6, and may broadly comprise a receiver 302, and a signalprocessor 304. The apparatus 300 may be used in a communications systemwherein noise is present—specifically noise that varies with time or thetype or status of the communications equipment being used. Thecommunications system may utilize a symbol-based encoding or modulationscheme and may include a channel, such as a data storage channel with amemory storage device. The apparatus 300 may classify the communicationssignal 18 as including noise and a predetermined type of event, such asa plurality of symbols being subjected to predetermined channel noiseregimes, with each regime being represented by one or more of aplurality of temporal, or time-based, attributes.

The receiver 302 generally receives the communications signal 18 andperforms initial processing on it. The receiver 302 may include aplurality of attribute extractors 306, a plurality of density functionelements 308, and a plurality of sensors 310. The attribute extractors306 and the density function elements 308 function in combination tocalculate or determine an estimate of a density function, such as a PDFor a CDF, of the communications signal 18, and may extract attributessuch as an average level or peak level of the signal. The attributeextractors 306 and the density function elements 308 may each includedigital electronic circuits, analog active and/or passive electroniccircuitry, such as operational amplifiers, filters, transistors,discrete components, and the like, or combinations thereof. In someembodiments, the attribute extractors 306 and the density functionelements 308 may further include the circuit 10, the circuit 100, orcombinations thereof.

The sensors 310 generally sense the communications signal 18 atdifferent points as represented on a plot of the density function. Thesensors 310 may include analog active and/or passive electroniccircuitry, such as operational amplifiers, filters, transistors,discrete components, and the like, or combinations thereof. The sensors310 may further sense or sample the density function at a plurality ofdifferent frequencies.

The signal processor 304 generally processes the signal from thereceiver 302 in order to classify the signal. The signal processor 304may include processors, microprocessors, microcontrollers, DSPs, FPGAs,analog and/or digital ASICs, or the like, or combinations thereof. Thesignal processor 14 may generally execute, process, or run instructions,code, code segments, software, firmware, programs, applications, apps,processes, services, daemons, or the like, or may step through states ofa finite-state machine. In some embodiments, the signal processor 304may be or may include the signal processor 14.

The signal processor 304 may be configured or programmed to determine aminimum number and a maximum number of channel noise regimes to whichthe communications signal 18 is subjected by utilizing a clusteringtechnique. The clustering technique may include a method of moments,hierarchical clustering, centroid-based clustering (k-means),distribution-based clustering, density based clustering, MorphologicalGranulometry Density (GSD), Scaled Invariant Feature Transform (SIFT),or the like. The clusters may be initialized corresponding to thesampled points of the density function from the receiver 302, based onthe minimum and maximum numbers of channel noise regimes. A signal valuewhich corresponds to each of the channel noise regimes may be processedby updating parameters such as , but not limited to, the power level,harmonic decomposition, ratio of harmonics, frequency response, or thelike, that are maintained for a cluster or group of clusters. Aplurality of statistical values, such as, but not limited to, the mean,the standard deviation, percentiles, or the maximum deviation, arecalculated from the updated parameters. The signal from the receiver 302may be classified based on the statistical values and the updatedparameters.

In other embodiments, clusterization or classification of thecommunications signal 18 based a density function can be used todetermine the channel status for memory channels. This is especiallyimportant for channels that exhibit cyclostationarity such as power gridsystems. A channel having statistical properties that vary cyclicallywith time is called a cyclostationary process. Typically, noise spikesgenerated by a equipment connected to powerline can be considered atemporary change in channel status. The memory channel can be modeledwith a finite state Markov chain. The two-state (G for good and B forburst) Markov model is referred to as the Gilbert-Elliott model. Inaddition, noise generated by the cyclo-stationary sources may be modeledas a memory channel. Furthermore, state characterization of the data maybe performed using clustering techniques of samples of the densityfunctions over one or more finite time periods.

When utilizing the density functions, the number and location of PDFsample points (or equivalently quantiles of the CDF) is an importantelement. The PDF may be considered a waveform with a frequency spectrum.This spectrum may be estimated through Fourier transform of the PDF orthe Fourier transform of the PDF repeated with a period T. The waveformmay be sampled, according to the Nyquist theorem, at twice the maximumbandwidth to have enough points to exactly reconstruct the signal. Thus,the informational content (entropy) of this sampling is optimal. Thiscan yield an oversampling of the density functions for a specificapplication. In some embodiments, clusterization can also be used tofind out which samplings points are the most appropriate to do thecharacterization of the PDF. The density function of the PDF may be usedto find the right sampling points.

In certain embodiments, density function centric signal processing canalso be used with vectorial signals, matrixed signals, or signals thathave more than one dimension. One embodiment applies density functionestimation to processing of signals on power lines in conjunction withpower measurements. These signals are carried by a combined signal andpower channel. One component is the active Power P(t), another componentthe reactive power Q (t), the third component the signal level carriedover the metal wire S(t). Examples of these types of systems are shownin FIGS. 7-9.

Another embodiment expands density function estimation to processing ofsignals on power lines in conjunction with power and radiatedelectromagnetic interference (EMI). These signals are carried by acombined signal, power, and wireless channel. A first component is theactive Power P(t). A second component is the reactive power Q(t). Athird component is the signal level carried over the metal wire S(t). Afourth component is the high frequency radiated power R(t). Examples ofthese types of systems may also be shown in FIGS. 7-9. For the precedingembodiments, the active and reactive powers are considered as signals.These signals can be transformed for the purpose of signal processing,including the power law.

Another embodiment implements a density function equalization to ensurethat the noise processed by the receiver is Gaussian, thus enablingtraditional receiver subsystems to remain unchanged. This isaccomplished by estimating the density function of the entire signal,removing a baseline value for the expected signals, then applying anhomomorphic transformation to the signal that the resulting signal has anoise with a Gaussian distribution.

At least a portion of the steps of a method 1000, in accordance with afifth embodiment of the current invention, of DC offset estimation isshown in FIG. 10. The steps of the method 1000 may be performed in theorder as shown in FIG. 10, or they may be performed in a differentorder. Furthermore, some steps may be performed concurrently as opposedto sequentially. In addition, some steps may not be performed.

Referring to step 1001, a plurality of quantile values 16 of an inputsignal is determined. The input signal may be a communications signal 18that is received by a circuit 100 for fine DC offset estimation. Thequantile value 16 is a value of a certain attribute of the signal suchas the voltage, the current, the power, or the like. The probabilitythat the value of the attribute of the signal is less than the quantilevalue 16 is inversely proportional to the number n of quantile values16, as seen in FIG. 3. The circuit 100 may include a DC offset estimator104 with a quantile value circuit 12 which determines or calculates thequantile values 16 as described above.

Referring to step 1002, a weighted average of the quantile values 16 iscalculated. The DC offset estimator 104 of circuit 100 may also includea signal processor 14 which is configured to calculate or determine theweighted average of the quantile values 16.

Referring to step 1003, the communications signal 18 is compensatedusing the weight average. The signal processor 14 may be configured toapply a weighted average compensation to the communications signal 18.

Referring to step 1004, the compensated communications signal 18 isscaled. The circuit 100 may further include the adaptive prescaler 106which may reduce the frequency of the communications signal 18 by aninteger factor.

At least a portion of the steps of a method 1100, in accordance with asixth embodiment of the current invention, of processing a noise signalwith functions including automatic squelch, automatic gain control(AGC), equalization, and dynamic range control (DRC) is shown in FIG.11. The steps of the method 1100 may be performed in the order as shownin FIG. 11, or they may be performed in a different order. Furthermore,some steps may be performed concurrently as opposed to sequentially. Inaddition, some steps may not be performed.

Referring to steps 1101 and 1102, a density function of a noise signalis extracted, and an attribute of the density function is extracted. Theapparatus 300 may include the receiver 302 with a plurality of attributeextractors 306 and density function elements 308. The noise signal maybe received from an external noise detector or the noise detector 204and may be coupled to the apparatus 300 so that the density functionelements 308 produce a density function and the attribute extractors 306extract various attributes from the density function waveform. Theattributes may correspond to average, quantile, or peak levels of thenoise signal. The receiver 302 may further include a plurality ofsensors 310 that sample the density function waveform at differentpoints on the waveform and at different frequencies.

Referring to step 1103, a unidirectional density function signal levelas a function of the attribute is produced. The density function signallevel is generally related to the density function and may includequantile levels, peak levels, average levels, or the like.

Referring to step 1104, the unidirectional density function signal levelis stored. The apparatus 300 may further include a memory storageelement such as random-access memory, sample and hold circuits,flip-flops, or the like, or combinations thereof.

Referring to steps 1105, 1106, and 1107, a density function of the noisesignal is extracted, an attribute of the density function is extracted,and a unidirectional density function signal level as a function of theattribute is produced. These steps are performed after theunidirectional density function signal level is stored. In addition,these steps may be performed in a continuous and/or real-time fashion asthe noise signal is repeatedly sampled.

Referring to step 1108, the most recently produced unidirectionaldensity function signal level is compared to the stored unidirectionaldensity function signal level. The apparatus 300 may include the signalprocessor 304 which may retrieve the stored unidirectional densityfunction signal level from the memory storage element and may comparethe two levels.

Referring to step 1109, a control signal is generated when the mostrecently produced unidirectional density function signal level is afunction of the stored unidirectional density function signal level. Asan example, the signal processor 304 may generate a squelch signal ifthe most recently produced unidirectional density function signal levelis greater than the stored unidirectional density function signal level.Alternatively, the signal processor 304 may generate a signal toattenuate or amplify a secondary signal such as an audio signal, a videosignal, a communications signal, or the like. Alternatively, the signalprocessor 304 may amplify a frequency sub-band of a signal.

At least a portion of the steps of a method 1200, in accordance with aseventh embodiment of the current invention, of decoding a signalreceived from a channel is shown in FIG. 12. The steps of the method1200 may be performed in the order as shown in FIG. 12, or they may beperformed in a different order. Furthermore, some steps may be performedconcurrently as opposed to sequentially. In addition, some steps may notbe performed.

Referring to step 1201, a density function of a received signal isdetermined. The signal from the channel may be received by the decoderapparatus 200 which includes a noise detector 204 that determines orestimates a density function of a signal.

Referring to steps 1202 and 1203, a bias attribute based on the densityfunction is determined and a reliability bias based on the biasattribute is determined. the noise detector 204 may map the densityfunction to a specific channel or noise source condition based on one ormore attributes. The noise detector 204 may also introduce a biascomponent into the intrinsic or extrinsic information.

Referring to steps 1204 and 1205, the reliability bias is applied to areliability measure to estimate logic states of detected bits in thesignal, and data bits are generated as a function of the detected bits.The decoder apparatus 200 may further include a decoder 206, which maydetermine the logic states of the appended density function bit anddetected bits of the communications signal 18. The decoder 206 may alsoapply the appended density function bit reliability measures of thedetected bits in order to generate the data bits.

At least a portion of the steps of a method 1300, in accordance with aeighth embodiment of the current invention, of classifying a signal asone of noise and a predetermined type of event comprising a plurality ofsymbols subjected to predetermined channel noise regimes, each regimerepresented by one or more of a plurality of temporal attributes isshown in FIG. 13. The steps of the method 1300 may be performed in theorder as shown in FIG. 13, or they may be performed in a differentorder. Furthermore, some steps may be performed concurrently as opposedto sequentially. In addition, some steps may not be performed.

Referring to step 1301, a density function of a signal is estimated. Thesignal may be a communications signal 18 that is received by theapparatus 300 which includes a receiver 302 with a plurality of densityfunction elements 308. Each density function element 308 may calculateor determine an estimate of a density function.

Referring to step 1302, the density function is sampled at a pluralityof points. The receiver 302 of the apparatus 300 may include a pluralityof sensors 310 which sense the communications signal 18 at differentpoints as represented on a plot of the density function. The sensors 310may further sense or sample the density function at a plurality ofdifferent frequencies.

Referring to step 1303, a maximum number of the regimes and a minimumnumber of the regimes are determined through a clustering technique. Theapparatus 300 may further include a signal processor 304 that isconfigured to determine the maximum and minimum number of channel noiseregimes to which the communications signal 18 is subjected. The signalprocessor 304 is further configured to utilize any one of a plurality ofclustering techniques including a method of moments, hierarchicalclustering, centroid-based clustering (k-means), distribution-basedclustering, density based clustering, Morphological Granulometry Density(GSD), Scaled Invariant Feature Transform (SIFT), or the like.

Referring to step 1304, a plurality of clusters corresponding to thesampled points is initialized based upon the maximum and minimumnumbers. The signal processor 304 may further be configured toinitialize the clusters corresponding to the sampled points of thedensity function from the receiver 302, based on the minimum and maximumnumbers of channel noise regimes.

Referring to step 1305, a signal value corresponding to each of theregimes is processed by updating parameters maintained for a cluster.The signal processor 304 may further be configured to process, through aclustering technique, a signal value corresponding to each of theregimes by updating parameters maintained for a cluster. The parametersmay include the power level, harmonic decomposition, ratio of harmonics,and frequency response or the like.

Referring to step 1306, a plurality of statistical values from theparameters maintained for the clusters is calculated. The signalprocessor 304 may further be configured to calculate the statisticalvalues, such as the mean, the standard deviation, percentiles, or themaximum deviation, from the parameters maintained for the clusters.

Referring to step 1307, the signal based upon the plurality ofstatistical values and the parameters is classified. The signalprocessor 304 may further be configured to classify the signal basedupon the plurality of statistical values and the parameters.

Although the invention has been described with reference to theembodiments illustrated in the attached drawing figures, it is notedthat equivalents may be employed and substitutions made herein withoutdeparting from the scope of the invention as recited in the claims.

Having thus described various embodiments of the invention, what isclaimed as new and desired to be protected by Letters Patent includesthe following:

1. A circuit for direct current (DC) offset estimation, the apparatuscomprising: a quantile value circuit for determining a plurality ofquantile values of an input signal; and a signal processor forcalculating a weighted average of the quantile values to yield a DCoffset estimate.
 2. The circuit of claim 1, wherein the quantile valuecircuit includes a plurality of quantile filters, each quantile filterproducing a quantile value and including a comparator for receiving theinput signal, the quantile value, and a first control signal, thecomparator configured to compare the communications signal and theimpulse filtered signal and provide a difference signal, a level shifterfor receiving the difference signal and a second control signal, thelevel shifter configured to adjust the level of the difference signaland provide a shifted signal, a monotonic transfer function componentfor receiving the shifted signal, the monotonic transfer functioncomponent configured to determine the magnitude of the shifted signaland provide a transfer function signal, and a latched integrator forreceiving the transfer function signal, the latched integratorconfigured to suppress transient characteristics of the transferfunction signal and provide the quantile value.
 3. The circuit of claim2, wherein the comparator receives a parameter from the first controlsignal that determines a time period over which the comparator filtersthe communications signal.
 4. The circuit of claim 2, wherein thecomparator receives a parameter from the first control signal thatdetermines a response time during which the comparator functions.
 5. Acircuit for fine direct current (DC) offset estimation, the apparatuscomprising: a DC offset estimation circuit for providing a DC offsetestimate of an input signal; an adaptive prescaler for scaling the DCoffset estimate signal in order to provide a scaled signal; and aselector circuit for receiving the input signal and the scaled signaland providing the scaled signal to the DC offset estimation circuit inorder for the DC offset estimation circuit to perform a fine DC offsetestimation of the scaled signal.
 6. The circuit of claim 5, wherein theDC offset estimation circuit includes a quantile value circuit fordetermining a plurality of quantile values of the input signal, and asignal processor for calculating a weighted average of the quantilevalues to yield the DC offset estimate.
 7. The circuit of claim 6,wherein the quantile value circuit includes a plurality of quantilefilters, each quantile filter producing a quantile value and including acomparator for receiving the input signal, the quantile value, and afirst control signal, the comparator configured to compare thecommunications signal and the impulse filtered signal and provide adifference signal, a level shifter for receiving the difference signaland a second control signal, the level shifter configured to adjust thelevel of the difference signal and provide a shifted signal, a monotonictransfer function component for receiving the shifted signal, themonotonic transfer function component configured to determine themagnitude of the shifted signal and provide a transfer function signal,and a latched integrator for receiving the transfer function signal, thelatched integrator configured to suppress transient characteristics ofthe transfer function signal and provide the quantile value.
 8. Adecoder apparatus for receiving a signal from a channel medium, thedecoder apparatus comprising: a noise detector adapted to receive asignal from a channel density function estimation circuit, wherein thenoise detector estimates a signal density function, maps the densityfunction in one or more density function bit samples, and appends adensity function bit reliability measure to each bit sample; and adecoder that determines logic states of the appended density functionbit reliability measure and detected bits in the signal and applies thedensity function bit reliability measures of detected bits to generatedata bits.
 9. The decoder apparatus of claim 8, wherein the densityfunction includes a probability density function or a cumulativedistribution function.
 10. The decoder apparatus of claim 8, wherein thenoise detector introduces a bias attribute.
 11. The decoder apparatus ofclaim 10, wherein the noise detector maps the density function to aspecific channel or noise source condition based on one or moreattributes.
 12. An apparatus for classifying a signal as one of noiseand a predetermined type of event comprising a plurality of symbolssubjected to predetermined channel noise regimes, each regimerepresented by one or more of a plurality of temporal attributes, theapparatus comprising: a receiver configured to estimate a densityfunction of the signal and sample the density function at a plurality ofpoints; and a signal processor connected to the receiver and configuredto determine a maximum number of the regimes and a minimum number of theregimes through a clustering technique, initialize a plurality ofclusters corresponding to the plurality of sampled points, based uponthe maximum and minimum numbers, process, through a clusteringtechnique, a signal value corresponding to each of the regimes byupdating parameters maintained for a cluster of the plurality ofclusters, calculate a plurality of statistical values from the updatedparameters, and classify the signal based upon the plurality ofstatistical values and the updated parameters.
 13. The apparatus ofclaim 12, wherein the receiver includes a plurality of sensors, eachsensor configured to sample a waveform of the density function at adifferent point on the waveform.
 14. The apparatus of claim 13, whereineach sensor is further configured to sample the density function at adifferent frequency.
 15. A method of DC voltage offset estimation for aDC estimation circuit receiving an input signal, the method comprisingthe steps of: determining a plurality of quantile values of the inputsignal; calculating a weighted average of the quantile values;compensating the input signal using the weighted average; and scalingthe compensated input signal.
 16. The method of claim 15, wherein thequantile values are points taken at regular intervals of a cumulativedistribution function of the input signal.
 17. A method of processing anoise signal with functions including automatic squelch, automatic gaincontrol (AGC), equalization, and dynamic range control (DRC), the methodcomprising the steps of: extracting a density function of the noisesignal; extracting an attribute of the density function; producing aunidirectional density function signal level as a function of theattribute; storing the unidirectional density function signal level inresponse to a predetermined condition producing a density functionstored signal level; extracting the density function of the noise signalafter the unidirectional density function signal level is stored;extracting an attribute of the density function; producing aunidirectional density function signal level as a function of theattribute; comparing the most recently produced unidirectional signallevel to the stored signal level; and generating a control signal whenthe unidirectional signal level is a function of the stored signallevel.
 18. The method of claim 17, wherein the density function includesa probability density function or a cumulative distribution function.19. A method of decoding a signal received from a channel, the methodcomprising the steps of: determining a density function of a receivedsignal; determining a bias attribute based on the density function ofthe signal; determining a reliability bias for the bias attribute;applying the reliability bias to a reliability measure to estimate logicstates of detected bits in the signal; and generating user bits as afunction of the detected bits.
 20. The method of claim 19, wherein thedensity function includes a probability density function or a cumulativedistribution function.
 21. A method of classifying a signal as one ofnoise and a predetermined type of event comprising a plurality ofsymbols subjected to predetermined channel noise regimes, each regimerepresented by one or more of a plurality of temporal attributes, themethod comprising the steps of: estimating a density function of thesignal; sampling the density function at a plurality of points;determining a maximum number of the regimes and a minimum number of theregimes through a clustering technique; initializing a plurality ofclusters corresponding to the sampled points, based upon the maximum andminimum numbers; processing, through a clustering technique, a signalvalue corresponding to each of the regimes by updating parametersmaintained for a cluster; calculating a plurality of statistical valuesfrom the parameters maintained for the clusters; and classifying thesignal based upon the plurality of statistical values and theparameters.
 22. The method of claim 21, wherein the density functionincludes a probability density function or a cumulative distributionfunction.